Many types of industrial machines include rotating components that may suffer from unbalance conditions. During operation, such unbalance conditions may cause undesirable vibrational effects throughout the machine. For example, it is known that unbalance conditions in aircraft engines may result in unwanted acoustic noise and structural vibrations throughout the aircraft. It is therefore desirable to characterize and control unbalance conditions of rotating components of aircraft engines, as well as the unbalance conditions of the rotating components other types of industrial machines.
Considerable efforts have been devoted to the diagnosis and management of engine unbalance conditions. One conventional method practiced by engine manufacturers is to modify the locations where engine vibrations may be transferred to the aircraft structure in order to reduce structurally transmitted vibrations, including the installation and use of damped bearings and vibration isolators. Another conventional method is to regularly balance the rotating components of aircraft engines using weights at specific locations, similar to that common practice of balancing automotive wheels. Still other methods of diagnosing and managing engine unbalance conditions may involve computational analysis of vibrational data using software algorithms that strive to mathematically model and characterize such data. Such algorithms may then be used for the computational prediction and development of appropriate vibrational damping solutions (e.g. the selection and location of balancing weights). Such methods include, for example, those methods and systems disclosed in U.S. Pat. No. 6,027,239 issued to Ghassaei, U.S. Pat. No. 5,586,065 issued to Travis, U.S. Pat. No. 5,313,407 issued to Tiernan et al., and U.S. Pat. No. 5,172,325 issued to Heidari.
Although desirable results have been achieved using such prior art methods and systems, there is room for improvement. For example, one possible weakness of at least some prior art algorithms is that such algorithms employ linear equations to characterize the engine vibrational data, even though the vibrational data may include significant non-linear components. Possible sources of non-linear vibrational data in an aircraft may include engine rotor shaft coupling misalignments, imbalances in the compressor and turbine stages of the engine, squeeze film bearings, and inner shaft bearings that couple the high and low rotors, structural members and joints, attachment components, and other possible sources. Non-linear components of vibration from such sources may not be adequately modeled using prior art linear analysis methods.
Another possible drawback of most of the prior art methods and systems is that such methods typically strive to minimize the vibratory displacement of only a limited number of locations (commonly only two locations) on the aircraft engine. Even though the locations may be carefully chosen in an attempt to reflect the general condition of the aircraft engine, the practice of forcing only a limited number of locations to be at their lowest possible overall vibrational level does not guarantee that vibratory energy cannot flow into the wing and fuselage through other flow paths. Therefore, novel methods and systems for analyzing engine unbalance conditions that at least partially mitigate these adverse characteristics of the prior art methods would be useful.